Loki vs CloudWatch Logs vs Datadog Logs: Log Management in 2026

Log management is one of those infrastructure decisions that feels low-stakes until you're paying $8,000/month for CloudWatch and realize you could have run Loki for $200. Or you're on Loki and you've spent two weeks building alerting that Datadog would have given you in an afternoon.

Here's an honest comparison of the three platforms most teams end up choosing between.

What Each Tool Actually Is

Grafana Loki is a log aggregation system designed to be cheap. It doesn't index log content — only labels (like Kubernetes pod name, namespace, container). You ship logs via Promtail or the OpenTelemetry Collector, store them in S3 or GCS, and query with LogQL. It integrates natively with Grafana.

AWS CloudWatch Logs is the default logging service for anything running on AWS. It's automatic for Lambda, ECS, EKS (with Fluent Bit), and EC2. You pay per GB ingested and stored, and query with CloudWatch Logs Insights (a SQL-like language).

Datadog Logs is part of Datadog's full observability platform. It indexes everything, offers pattern detection, anomaly alerts, and ties logs to traces and metrics. It's also the most expensive of the three at scale.

Pricing at 10 GB/Day Ingestion

Datadog's pricing is per GB retained, not just ingested. With 15-day retention at 10GB/day = 150GB stored × $0.10 = $1,500/month at list price. Most teams negotiate or use Flex Logs for cheaper archival.

Loki is the clear winner on cost if you have the ops bandwidth to run it.

Query Language Comparison

LogQL (Loki):

# Filter logs from a specific namespace
{namespace="production", container="api"} |= "error"
 
# Parse JSON and filter on a field
{namespace="production"} | json | status_code >= 500
 
# Rate of errors over 5 minutes
rate({namespace="production"} |= "ERROR" [5m])

LogQL feels like Prometheus PromQL — familiar if you're already in the Grafana stack, unfamiliar otherwise.

CloudWatch Logs Insights:

fields @timestamp, @message
| filter @message like /ERROR/
| stats count(*) as errorCount by bin(5m)
| sort @timestamp desc
| limit 50

More SQL-like, easier for developers who haven't used Loki. But queries on large datasets can be slow and expensive — each query scans data and costs $0.005/GB.

Datadog Log Search:

service:api status:error @http.status_code:>=500

Datadog's query UI is the most user-friendly. Autocomplete, faceted search, saved queries. You don't write queries so much as click and filter. For teams where non-engineers need to look at logs, this is a significant advantage.

Integration With Your Existing Stack

If you're 100% AWS, CloudWatch wins on zero-friction integration. Lambda logs just appear. ECS tasks write directly to log groups. No agents to deploy.

If you're on Kubernetes, Loki with Promtail is easy to deploy and cheap.

If you want logs correlated with APM traces, Datadog is the only one that does this out of the box.

Alerting Capabilities

Loki: Create alerts in Grafana (Grafana Alerting) using LogQL. You can alert on error rate, log line patterns, or absence of expected logs. Integrate with PagerDuty, Slack, OpsGenie. Requires setting up the alert pipeline yourself.

CloudWatch Logs: Create metric filters to turn log patterns into CloudWatch Metrics, then alarm on those metrics. Works, but multi-step setup. CloudWatch Alarms → SNS → Lambda/email/Slack.

Datadog: Best alerting experience. One screen: pick a log query, set threshold, choose notification channel. Anomaly detection, composite alerts, SLO tracking built in.

Long-Term Retention Strategy

All three struggle with long-term retention costs:

• Loki: Archive to S3 with Loki's compactor. Query older logs with LogQL but slower. Realistic long-term cost: ~$3/GB/year on S3.
• CloudWatch: Set log group retention (e.g., 90 days) and export older logs to S3 with a Lambda + S3 lifecycle policy. Query old logs with Athena ($5/TB scanned).
• Datadog: Flex Logs tier for cheap archive storage (not queryable without rehydration). Rehydrating old logs costs extra.

Which Team Should Use What

No single answer. CloudWatch is the default for small AWS teams who don't want to think about it. Loki is the right answer for cost-sensitive Kubernetes teams who already have Grafana. Datadog is the right answer when you need the full observability stack and budget isn't the primary concern.